Using Twitter to Gauge Customer Satisfaction Response to a Major Transit Service Change in Calgary, Canada
Bibliographic record
Abstract
Measuring public opinion about the quality of transit services is a key factor in understanding and addressing customer dissatisfaction and increasing customer loyalty and ridership. Sentiment analysis using social media-in particular Twitter-is a relatively cheap and potentially powerful complement to traditional survey methods, which are expensive and limited in sample size. This study aims to evaluate customer response to the introduction of Calgary Transit's MAX routes. We compared customer satisfaction expressed on Twitter with measured service reliability in the form of on-time performance. We also employed a qualitative research approach using content analysis from Twitter to gauge rider satisfaction over several service attributes before and after the service change. A transit-specific sentiment lexicon was developed to support this study using a hybrid approach. This lexicon outperformed generic sentiment lexicons traditionally used in transit studies with regard to both accuracy (18.4%) and F1-score (7.1%). We found that the overall perception of on-time performance from riders using Twitter was similar to the actual performance in the field. This was also observed for one individual route on which stops with poor schedule adherence were linked with negative feedback. This study concludes that combining customer-oriented measures from Twitter with operational-oriented ones would enable transit agencies to make better-informed decisions for planning and operational purposes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".